%0 Journal Article
%J IEEE Transactions on Power Systems
%D 2015
%T Stochastically Optimized, Carbon-Reducing Dispatch of Storage, Generation, and Loads
%A Alberto J Lamadrid
%A Daniel L. Shawhan
%A Carlos E. Murillo-Sanchez
%A Ray D. Zimmerman
%A Zhu, Yujia
%A Daniel J. Tylavsky
%A Kindle, Andrew G.
%A Dar, Zamiyad
%K CERTS
%K reliability and markets
%K RM07-002
%X We present a new formulation of a hybrid stochastic-robust optimization and use it to calculate a look-ahead, security-constrained optimal power flow. It is designed to reduce carbon dioxide (CO2) emissions by efficiently accommodating renewable energy sources and by realistically evaluating system changes that could reduce emissions. It takes into account ramping costs, CO2 damages, demand functions, reserve needs, contingencies, and the temporally linked probability distributions of stochastic variables such as wind generation. The inter-temporal trade-offs and transversality of energy storage systems are a focus of our formulation. We use it as part of a new method to comprehensively estimate the operational net benefits of system changes. Aside from the optimization formulation, our method has four other innovations. First, it statistically estimates the cost and CO2 impacts of each generator's electricity output and ramping decisions. Second, it produces a comprehensive measure of net operating benefit, and disaggregates that into the effects on consumers, producers, system operators, government, and CO2 damage. Third and fourth, our method includes creating a novel, modified Ward reduction of the grid and a thorough generator dataset from publicly available information sources. We then apply this method to estimating the impacts of wind power, energy storage, and operational policies.
%B IEEE Transactions on Power Systems
%V 30
%P 1064 - 1075
%8 03/2015
%N 2
%! IEEE Trans. Power Syst.
%R 10.1109/TPWRS.2014.2388214
%0 Journal Article
%J IEEE Transactions on Smart Grid
%D 2013
%T Secure Planning and Operations of Systems With Stochastic Sources, Energy Storage, and Active Demand
%A Carlos E. Murillo-Sanchez
%A Ray D. Zimmerman
%A C. Lindsay Anderson
%A Robert J. Thomas
%K ancillary services
%K CERTS
%K power system planning
%K power system reliability
%K reliability and markets
%K renewables
%K RM07-002
%X This work presents a stochastic optimization framework for operations and planning of an electricity network as managed by an Independent System Operator. The objective is to maximize the total expected net benefits over the planning horizon, incorporating the costs and benefits of electricity consumption, generation, ancillary services, load-shedding, storage and load-shifting. The overall framework could be characterized as a secure, stochastic, combined unit commitment and AC optimal power flow problem, solving for an optimal state-dependent schedule over a pre-specified time horizon. Uncertainty is modeled to expose the scenarios that are critical for maintaining system security, while properly representing the stochastic cost. The optimal amount of locational reserves needed to cover a credible set of contingencies in each time period is determined, as well as load-following reserves required for ramping between time periods. The models for centrally-dispatched storage and time-flexible demands allow for optimal tradeoffs between arbitraging across time, mitigating uncertainty and covering contingencies. This paper details the proposed problem formulation and outlines potential approaches to solving it. An implementation based on a DC power flow model solves systems of modest size and can be used to demonstrate the value of the proposed stochastic framework.
%B IEEE Transactions on Smart Grid
%V 4
%P 2220 - 2229
%8 12/2013
%N 4
%! IEEE Trans. Smart Grid
%R 10.1109/TSG.2013.2281001
%0 Journal Article
%J Decision Support Systems
%D 2013
%T A stochastic, contingency-based security-constrained optimal power flow for the procurement of energy and distributed reserve
%A Carlos E. Murillo-Sanchez
%A Ray D. Zimmerman
%A C. Lindsay Anderson
%A Robert J. Thomas
%K CERTS
%K reliability and markets
%K reserve markets
%K RM07-002
%K smart grid
%X It is widely agreed that optimal procurement of reserves, with explicit consideration of system contingencies, can improve reliability and economic efficiency in power systems. With increasing penetration of uncertain generation resources, this optimal allocation is becoming even more crucial. Herein, a problem formulation is developed to solve the day-ahead energy and reserve market allocation and pricing problem that explicitly considers the redispatch set required by the occurrence of contingencies and the corresponding optimal power flow, static and dynamic security constraints. Costs and benefits, including those arising from eventual demand deviation and contingency-originated redispatch and shedding, are weighted by the contingency probabilities, resulting in a scheme that contracts the optimal amount of resources in a stochastic day-ahead procurement setting. Furthermore, the usual assumption that the day-ahead contracted quantities correspond to some base case dispatch is removed, resulting in an optimal procurement as opposed to an optimal dispatch. Inherent in the formulation are mechanisms for rescheduling and pricing dispatch deviations arising from realized demand fluctuations and contingencies. Because the formulation involves a single, one stage, comprehensive mathematical program, the Lagrange multipliers obtained at the solution are consistent with shadow prices and can be used to clear the day-ahead and spot markets of the different commodities involved.
%B Decision Support Systems
%V 56
%8 12/2013
%! Decision Support Systems
%R 10.1016/j.dss.2013.04.006
%0 Report
%D 2008
%T A "SuperOPF" Framework
%A Alberto J Lamadrid
%A Surin Maneevitjit
%A Timothy D. Mount
%A Carlos E. Murillo-Sanchez
%A Robert J. Thomas
%A Ray D. Zimmerman
%K Market mechanisms
%K reliability and markets
%K reliability management
%K RM05-003
%K SuperOPF
%X The objective of the SuperOPF project is to develop a framework that will provide proper allocation and valuation of resources through true co-optimization across multiple scenarios. Instead of solving a sequence of simpler and approximate sub-problems, the SuperOPF approach combines as much as possible into a single mathematical programming framework, with a full AC network and simultaneous co-optimization across multiple scenarios with stochastic costs.

This effort involves development of the problem formulations, implementation of research grade software codes, and testing of the methods and algorithms on a range of case studies to demonstrate their added value over currently available tools.

%P 59
%8 12/2008